CIOs and CTOs will provide a candid review of their first year of AI in production: which bets they would fund again in 2027, which they shut down, and why. Expect insights into budgets, adoption, and the organizational friction nobody planned for. The moderator sets context and the panel sets the baseline every later session builds on.

AI programs stall because the data is never ready, and teams wait for a clean state that never arrives. This session breaks that cycle: consolidate sources, harmonize definitions, and earn trust through quality, classification, and discoverability. DwyerOmega shows how it consolidated 32 ERPs onto Microsoft Fabric in seven to eight months with AI-led coding, giving leadership one view of the business while acquisitions keep coming, plus the playbook for the next deal.

Every CIO carries a core platform too risky to touch and too expensive to keep. This session shows how AI shortens the planning: legacy code, stored procedures, configuration, user stories, and tickets mapped into AS-IS and TO-BE knowledge graphs, so leaders decide what to retire, carry forward, or build before funding the program. KinderCare's assessment traced 501 features across six domains and tested the plan against 240 curated questions.

Most engineering teams are still at the Copilot stage. This session walks the AI SDLC maturity model level by level: AI assist, vibe coding, spec-driven development, AI-native delivery on a code knowledge graph, and agentic engineering, with the roles and skills each level needs. Two short client proofs follow: Conservice shipped two years of delivery in four months; Hubexo runs governed agents that check scope on every ticket.

Back-office work is where agents pay back first and where a wrong decision costs the most. Atomic Work and a customer CIO show what a mixed workforce of people and AI agents looks like in live ITSM, invoicing, onboarding, and dispute resolution: which work agents own, where people keep the decision, and how both are measured. Accion client examples add before-and-after numbers. No product demos.

Governance stops the wrong AI; a center of excellence gets the right AI built. American Nurses Association (ANA) shows how AI Prism collected AI needs from across the business, ranked them on value and feasibility, and shipped the best into production. A second client shows a Scrum team enabled for AI development in one day: method, environment, and standards. Attendees leave with a pilot-to-production checklist and a plan to train their teams.

Do you know your AI footprint: what your agents cost, what data they touch, and who owns each one? Accion walks through AEGIS, one inventory of agents across clouds, apps, and data platforms, with controls from identity to audit. Client leaders then test it against their own deployments: who is accountable when an agent is wrong, how cost is attributed, and what scoping authority comes before scaling.

The closing panel asks what it takes to keep AI running once it leaves the pilot. A data and AI leader from Hyperion MT opens with a short view on a trusted data foundation. Client leaders then compare what broke when AI met production: data and team silos, readiness gaps, and who is accountable when an agent gets it wrong. Each panelist names one rule they now apply before approving the next AI initiative.

AI is changing what technology teams do and what they buy. Client leaders discuss how the CIO and CTO role is shifting from cost center to growth partner, how team shape and skills change under AI-assisted delivery, which capabilities they build in-house versus source, and how they will pay vendors in 2027: headcount, outcomes, or both. The panel closes the content day with practical advice on reskilling.